Notes on dataarchitecture.
Short essays on architecture, cost, hiring and AI: one cartoon, one idea, most days of the week. No tutorials, no listicles.
Sizing Your Orchestrator To Your Team, Not Your Ambition
Three engineers maintaining Kubernetes Airflow for 20 DAGs is a sizing mistake, and a common one.
Read →The Stack You Actually Need At 15 Engineers
At 15 engineers, a data stack with twelve tools is mostly twelve invoices and one tired person.
Read →What a Broken Pipeline Actually Costs Per Week
Pull the last four weeks of pipeline incidents and put an hour next to each one. The total is rarely what you guessed.
Read →Coalesce Context As Infra
Treat your metric definitions like a Terraform file, or keep the agent away from them.
Read →DE Role Evolution 2026
If a model writes the pipelines, I don't know yet where your juniors learn why pipelines break.
Read →Semantic For Unstructured First
Most AI data problems I see start in a column called payload, long before any embedding.
Read →Orchestration Consolidation Matrix
Count the places a job can be scheduled in your data stack. Include the cron on that one VM.
Read →BigQuery Scan Cost Surprises
LIMIT 10 doesn't reduce what BigQuery scans on an unclustered table. A lot of teams learn that from the invoice.
Read →dbt Coalesce Metadata Shift
The description fields in your dbt project used to be read by nobody. Now an agent reads them.
Read →Governance Before AI Act Fines
The provenance record behind an AI feature fits in a YAML file next to the pipeline code.
Read →Want expert eyes on your data architecture?
No pitch. An honest conversation about whether I can help, and what shape it would take if I can.